FEATURE

Faster

Although the trophy awarded to the winner of the 34th America’s Cup in San Francisco Bay may be the oldest in international sports, there’s nothing vintage about modern competitive sailing technology. As ORACLE TEAM USA CEO Russell Coutts says, the 2013 America’s Cup is designed to meet the expectations of the Facebook generation, not the Flintstones generation. And fittingly, ORACLE TEAM USA has turned to information technology as a key tool for defending the Cup.

ORACLE TEAM USA’s multipronged technology strategy starts with the performance data the team collects from every training run. “The absolute core of our technical performance analysis is based on collecting information while sailing and processing it to provide useful feedback to the sailors and designers to improve the design and performance of the boat,” says Ian “Fresh” Burns, design team coordinator.

More than 300 sensors all over the boat collect a huge amount of raw data, which is transmitted to a server in the hull. Sensors measure the strain on the mast, hull, and wing; monitor the load generated on components ranging from the jib to the winches; and monitor the effectiveness of each change made by the trimmers, who constantly adjust the sail wing to fully exploit wind conditions. The boat generates more than 3,000 data variables about 10 times a second when sailing. ORACLE TEAM USA also runs several video feeds and takes still images of the sails every second.

A typical training run generates about a gigabyte of raw performance data as well as 150 to 200 gigabytes of video, says Asim Khan, director of IT for ORACLE TEAM USA. Khan downloads the performance data at the end of each day to the team’s onshore Exadata Database Machine X3-2, adding it to about 80 gigabytes of weather information, boat data, and performance metadata from the current quest to defend the America’s Cup in 2013 as well as a cache of historical data from previous Cup campaigns.

Extreme Database Performance

With speed being an essential element of success, ORACLE TEAM USA turned to the record-breaking performance of Oracle’s Exadata Database Machine X3-2.

Although the team has had the system only since April 2013, ORACLE TEAM USA Director of IT Asim Khan already sees dramatic system performance improvement from the use of Oracle Exadata. “When you look at CPU-intensive tasks, there is roughly a tenfold speed improvement, and I/O-intensive tasks have improved by roughly 20 percent,” says Khan, who expects that performance will only get faster.

For Khan, however, the real satisfaction lies in how quickly he can get critical information to weary sailors as they come off the water. “When they come back, they want to look at the numbers and get some objective sense of whether things were running well or not, so we’ll look for standout variables in a certain time period to validate whatever they felt out on the water,” he says.

The old systems took 30 to 40 minutes to collect, import, and run the reports—a long wait for sailing team members who have already put in a full day. But it’s important to get them that data while the sail is still fresh in their memories, says Khan. “If you wait until the next day, they will have lost a lot of information that was fresh in their memories. So getting that time from 40 minutes down to 10 is critical, and Oracle Exadata makes a huge difference.”

The team uses the treasure trove of raw performance data, as well as the videos and still images, for performance analysis and deeper analytical dives into historical data. The trick is to tailor the information flow to fit each situation. “We do anything we can to make the information easy and simple to consume,” says Burns. “It brings the awareness level up and the crisis decision-making down.”

Real-Time Analytics

ORACLE TEAM USA’s AC72s sail every day with a chase boat that serves as the real-time analytical hub. The four-man performance team configures a feed of about 150 key parameters, which is transmitted in real time to the Oracle Database instance on the performance chase boat. The team members can also connect to the onshore Exadata Database Machine X3-2 via a 4G connection, enabling them to access historical data for comparative analytics.

The performance team runs a variety of analyses, all geared to optimize boat performance, and feeds that information to the ORACLE TEAM USA sailors via radio. One team member analyzes data from the sails and wing, another looks for data trends, a system tech monitors the system itself, and Burns looks at the data from a sailor’s point of view.

“We constantly check the numbers, from the configuration of the boat to monitoring wind and sea conditions during a test, making sure the test results are good, and finally feeding the test results straight back to the sailing team,” says Burns. “Sometimes the analysis requires a very complicated combination of 10, 20, or 30 variables run through a time-based algorithm, to give us predictions on what will happen in the next few seconds, minutes, or even hours in terms of weather analysis.”

Real-time analytics usage extends to the sailing crew. ORACLE TEAM USA sailors wear ruggedized PDAs on their forearms or wrists and receive a real-time, customized feed of information to help improve sailing performance. There are also several tablet devices in fixed places around the boat that display more-general data such as wind speed. “We are always at the red line for things like loading on the boat, and it’s critical that we don’t go over,” says Skipper Jimmy Spithill. “The fact that we can have live data instantaneously is key.”

Sailor Gilberto Nobili—a grinder who cranks the winches that power components such as the sail wing—programs all the PDAs and tablet devices in Java, which he likes for its extensibility. “I write the code once, and it can be used on many different devices,” he says. He also needs lightweight code that can maintain a high refresh rate for as many as 30 devices without clogging the wireless network. “We drive the boat basically on numbers, so it’s a big problem to have information that is even seconds late due to connection problems, particularly because the boat now flies on [thin daggerboards called] foils. The foiling requires real-time information that needs to be really accurate.”

Onshore Analytics

Once the race boats dock, their servers, as well as the database on the performance boat, are synced with the Oracle Database instance on the team’s Exadata Database Machine X3-2.

The data comes into the Exadata Database Machine X3-2 in two different ways, says Khan, who collects, downloads, and scrubs the data to make it ready for analysis. A small chunk comes in live via a 4G connection, and Khan downloads the rest when the boats return to the base. He also synchronizes performance data from the Oracle Database instance on the chase boat with the onshore system. Khan speeds up the process by using external tables, which enable him to map a table to a file and load from that. “Our data sets change daily as the variables we measure change,” he says. “External tables load dynamic data, so we don’t have to recompile procedures.”

Khan looks at Oracle Database as the backbone of the operation. “We use it as a sort of centralized management tool, with lots of ways to access it, from traditional queries and custom-built tools to Oracle Application Express–based web pages and mobile apps,” he says.

Race Cutter

The most widely used tool is Race Cutter, a custom application that pulls sensor data from the Exadata Database Machine X3-2, with added metadata markers that synchronize the video, photos, and audio streams with the raw numbers.

Team members can click to a certain moment and view all the pertinent information from that time stamp. “It’s a classic example of a tool used by many people for many different reasons,” says Javier Cuevas Domingo, computer engineer for the performance group.

The fact that we can have live data instantaneously is key.

Jimmy Spithill,
Skipper, ORACLE TEAM USA

For example, the design or sailing teams can look at information from a specific point in time and analyze any number of performance factors, such as the strain on the daggerboards or the load on a rope. “If you click a certain point in time, you’ll jump to the images and video from a number of different camera viewpoints,” says Burns.

The team also uses Race Cutter as a debriefing tool for each training session. The team gathers around three big-screen TVs to see how the boat can be sailed better. Clicking highlighted time stamps called Events, the team can listen to comments from coach Philippe Presti about those points in time or review a single testing sequence.

“We can compare today’s data with that of other days as well,” says Domingo. “It’s also good to correlate the subjective impression of the sailors with the data. It helps them understand what was going on.”

Traditional Queries

Khan also builds reports to help designers and sailors solve the challenges of one-boat testing. The new boat class means that most teams have had only one boat for a significant portion of the training schedule. Instead of comparing data from two boats under sail, the performance analysis has to be done numerically by comparing data sets. One estimate is that with one-boat testing, you need to collect 40 times as much data to get good results.

“What we do is compare sailing data on different days or compare it with target data we have generated,” says Khan, who fields a large variety of requests, ranging from how daggerboards perform under certain configurations to comparing boat performance at certain wind speed ranges. He cites a recent request to build a report that examines the rudder usage during maneuvers as an example. “I first identified the maneuvers in the data set and then analyzed how the rudder was used—the magnitude of the rudder angle and the rate of change,” he says. “By looking at the average for a long period of time, as well as peak values, we can design a more efficient rudder.”

Easy Access with Oracle Application Express

ORACLE TEAM USA has also turned to Oracle Application Express to make information easier and simpler to consume. For example, one application simplifies quality control on the data sets the team generates through performance tests—short, timed, straight-line bursts of sailing that measure a wide range of parameters. The team may run 60 to 70 tests a day, using each test as a data point.

Khan uses Oracle Application Express to automate the second level of quality control, creating web pages that crew members use to check and correct a lot of what he calls the metadata—data that is manually input by the performance team about the tests themselves: when they started and stopped, what the wing sail trim was during the test, and so on. “It’s essentially data that describes what actions went on out in the water,” he says.

Next Steps

The sailors each vet the data on their pertinent pages. For example, a sail trimmer’s web page lists all the sail changes during a training run, along with the formal test periods that occurred during that time. He’ll be able to see if something doesn’t look right—a certain sail that shouldn’t be up on an upwind tack, for example—and can research and make the change. “The Oracle Application Express page gives him the interface for changing those manual inputs and then processing the data to correct it,” Khan says.

And best of all, it’s a “set and forget” effort. “I can write a query and put it on a web page with Oracle Application Express,” Khan says. “Whenever users load the page, the query is refreshed, and I don’t have to do anything.”

Sailors also have access to an Oracle Application Express–based mobile app that automates the 250-item checklist necessary to prep the boat for sailing. “It’s a perfect use of database and mobile technology,” says Burns. “Oracle Application Express is really a powerful tool for widespread mobile data access.”

With data usage proliferating across all parts of the team, the real measure of success is the boat’s performance. “We measure performance as a function of time,” says Khan. “From this boat launch to race time, we’ve seen a 20 to 30 percent improvement in pure speed around the course. Older [less data-driven] race campaigns saw much smaller improvements.”